Refine
Has Fulltext
- yes (4504) (remove)
Year of publication
Document Type
- Postprint (2089)
- Doctoral Thesis (1693)
- Monograph/Edited Volume (221)
- Preprint (126)
- Working Paper (120)
- Article (86)
- Master's Thesis (58)
- Habilitation Thesis (38)
- Part of Periodical (26)
- Conference Proceeding (25)
Language
- English (4504) (remove)
Is part of the Bibliography
- yes (4504) (remove)
Keywords
- climate change (69)
- Klimawandel (47)
- machine learning (40)
- Modellierung (34)
- diffusion (28)
- morphology (27)
- climate (25)
- model (25)
- Germany (24)
- German (22)
Institute
- Institut für Biochemie und Biologie (531)
- Institut für Physik und Astronomie (523)
- Mathematisch-Naturwissenschaftliche Fakultät (482)
- Institut für Geowissenschaften (445)
- Institut für Chemie (363)
- Humanwissenschaftliche Fakultät (207)
- Extern (205)
- Strukturbereich Kognitionswissenschaften (174)
- Wirtschaftswissenschaften (169)
- Institut für Mathematik (166)
Improving permafrost dynamics in land surface models: insights from dual sensitivity experiments
(2024)
The thawing of permafrost and the subsequent release of greenhouse gases constitute one of the most significant and uncertain positive feedback loops in the context of climate change, making predictions regarding changes in permafrost coverage of paramount importance. To address these critical questions, climate scientists have developed Land Surface Models (LSMs) that encompass a multitude of physical soil processes. This thesis is committed to advancing our understanding and refining precise representations of permafrost dynamics within LSMs, with a specific focus on the accurate modeling of heat fluxes, an essential component for simulating permafrost physics.
The first research question overviews fundamental model prerequisites for the representation of permafrost soils within land surface modeling. It includes a first-of-its-kind comparison between LSMs in CMIP6 to reveal their differences and shortcomings in key permafrost physics parameters. Overall, each of these LSMs represents a unique approach to simulating soil processes and their interactions with the climate system. Choosing the most appropriate model for a particular application depends on factors such as the spatial and temporal scale of the simulation, the specific research question, and available computational resources.
The second research question evaluates the performance of the state-of-the-art Community Land Model (CLM5) in simulating Arctic permafrost regions. Our approach overcomes traditional evaluation limitations by individually addressing depth, seasonality, and regional variations, providing a comprehensive assessment of permafrost and soil temperature dynamics. I compare CLM5's results with three extensive datasets: (1) soil temperatures from 295 borehole stations, (2) active layer thickness (ALT) data from the Circumpolar Active Layer Monitoring Network (CALM), and (3) soil temperatures, ALT, and permafrost extent from the ESA Climate Change Initiative (ESA-CCI). The results show that CLM5 aligns well with ESA-CCI and CALM for permafrost extent and ALT but reveals a significant global cold temperature bias, notably over Siberia. These results echo a persistent challenge identified in numerous studies: the existence of a systematic 'cold bias' in soil temperature over permafrost regions. To address this challenge, the following research questions propose dual sensitivity experiments.
The third research question represents the first study to apply a Plant Functional Type (PFT)-based approach to derive soil texture and soil organic matter (SOM), departing from the conventional use of coarse-resolution global data in LSMs. This novel method results in a more uniform distribution of soil organic matter density (OMD) across the domain, characterized by reduced OMD values in most regions. However, changes in soil texture exhibit a more intricate spatial pattern. Comparing the results to observations reveals a significant reduction in the cold bias observed in the control run. This method shows noticeable improvements in permafrost extent, but at the cost of an overestimation in ALT. These findings emphasize the model's high sensitivity to variations in soil texture and SOM content, highlighting the crucial role of soil composition in governing heat transfer processes and shaping the seasonal variation of soil temperatures in permafrost regions.
Expanding upon a site experiment conducted in Trail Valley Creek by \citet{dutch_impact_2022}, the fourth research question extends the application of the snow scheme proposed by \citet{sturm_thermal_1997} to cover the entire Arctic domain. By employing a snow scheme better suited to the snow density profile observed over permafrost regions, this thesis seeks to assess its influence on simulated soil temperatures. Comparing this method to observational datasets reveals a significant reduction in the cold bias that was present in the control run. In most regions, the Sturm run exhibits a substantial decrease in the cold bias. However, there is a distinctive overshoot with a warm bias observed in mountainous areas. The Sturm experiment effectively addressed the overestimation of permafrost extent in the control run, albeit resulting in a substantial reduction in permafrost extent over mountainous areas. ALT results remain relatively consistent compared to the control run. These outcomes align with our initial hypothesis, which anticipated that the reduced snow insulation in the Sturm run would lead to higher winter soil temperatures and a more accurate representation of permafrost physics.
In summary, this thesis demonstrates significant advancements in understanding permafrost dynamics and its integration into LSMs. It has meticulously unraveled the intricacies involved in the interplay between heat transfer, soil properties, and snow dynamics in permafrost regions. These insights offer novel perspectives on model representation and performance.
Today, near-surface investigations are frequently conducted using non-destructive or minimally invasive methods of applied geophysics, particularly in the fields of civil engineering, archaeology, geology, and hydrology. One field that plays an increasingly central role in research and engineering is the examination of sedimentary environments, for example, for characterizing near-surface groundwater systems. A commonly employed method in this context is ground-penetrating radar (GPR). In this technique, short electromagnetic pulses are emitted into the subsurface by an antenna, which are then reflected, refracted, or scattered at contrasts in electromagnetic properties (such as the water table). A receiving antenna records these signals in terms of their amplitudes and travel times. Analysis of the recorded signals allows for inferences about the subsurface, such as the depth of the groundwater table or the composition and characteristics of near-surface sediment layers. Due to the high resolution of the GPR method and continuous technological advancements, GPR data acquisition is increasingly performed in three-dimensional (3D) fashion today.
Despite the considerable temporal and technical efforts involved in data acquisition and processing, the resulting 3D data sets (providing high-resolution images of the subsurface) are typically interpreted manually. This is generally an extremely time-consuming analysis step. Therefore, representative 2D sections highlighting distinctive reflection structures are often selected from the 3D data set. Regions showing similar structures are then grouped into so-called radar facies. The results obtained from 2D sections are considered representative of the entire investigated area. Interpretations conducted in this manner are often incomplete and highly dependent on the expertise of the interpreters, making them generally non-reproducible.
A promising alternative or complement to manual interpretation is the use of GPR attributes. Instead of using the recorded data directly, derived quantities characterizing distinctive reflection structures in 3D are applied for interpretation. Using various field and synthetic data sets, this thesis investigates which attributes are particularly suitable for this purpose. Additionally, the study demonstrates how selected attributes can be utilized through specific processing and classification methods to create 3D facies models. The ability to generate attribute-based 3D GPR facies models allows for partially automated and more efficient interpretations in the future. Furthermore, the results obtained in this manner describe the subsurface in a reproducible and more comprehensive manner than what has typically been achievable through manual interpretation methods.
The dynamic landscape of digital transformation entails an impact on industrial-age manufacturing companies that goes beyond product offerings, changing operational paradigms, and requiring an organization-wide metamorphosis. An initiative to address the given challenges is the creation of Digital Innovation Units (DIUs) – departments or distinct legal entities that use new structures and practices to develop digital products, services, and business models and support or drive incumbents’ digital transformation. With more than 300 units in German-speaking countries alone and an increasing number of scientific publications, DIUs have become a widespread phenomenon in both research and practice.
This dissertation examines the evolution process of DIUs in the manufacturing
industry during their first three years of operation, through an extensive longitudinal single-case study and several cross-case syntheses of seven DIUs. Building on the lenses of organizational change and development, time, and socio-technical systems, this research provides insights into the fundamentals, temporal dynamics, socio-technical interactions, and relational dynamics of a DIU’s evolution process. Thus, the dissertation promotes a dynamic understanding of DIUs and adds a two-dimensional perspective to the often one-dimensional view of these units and their interactions with the main organization throughout the startup and growth phases of a DIU.
Furthermore, the dissertation constructs a phase model that depicts the early stages of DIU evolution based on these findings and by incorporating literature from information systems research. As a result, it illustrates the progressive intensification of collaboration between the DIU and the main organization. After being implemented, the DIU sparks initial collaboration and instigates change within (parts of) the main organization. Over time, it adapts to the corporate environment to some extent, responding to changing circumstances in order to contribute to long-term transformation. Temporally, the DIU drives the early phases of cooperation and adaptation in particular, while the main organization triggers the first major evolutionary step and realignment of the DIU.
Overall, the thesis identifies DIUs as malleable organizational structures that are crucial for digital transformation. Moreover, it provides guidance for practitioners on the process of building a new DIU from scratch or optimizing an existing one.
The present thesis looks at cultural conceptualisations in relation to DEATH in Irish English from a Cultural Linguistic perspective and puts a special focus on the diachronic development of these conceptualisations. For the study, a corpus consisting of 1,400 death notices from the Dublin-based national newspaper The Irish Times from 14 historical periods between 1859 and 2023 was compiled, resulting in a highly specialised 70,000-word corpus. First, the manual qualitative analysis of the death notices produced evidence for eight superordinate cultural conceptualisations surrounding DEATH, namely, in the order of their frequency THE DEAD ARE TO BE REMEMBERED OR REGRETTED, DEATH IS SOMETHING POSITIVE, DEATH IS REST, DEATH IS A JOURNEY, DYING IS THE BEGINNING OF ANOTHER LIFE, DEATH IS (NOT) A TABOO, DEATH IS GOD’S WILL, and DEATH IS THE END. These conceptualisations were derived from linguistic expressions in the death notices that have these conceptualisations as a cognitive basis. Second, the quantitative comparison of the individual conceptualisations detected diachronic variation, which is interconnected with historical and social developments in Ireland. The thesis, therefore, illustrates the applicability of Cultural Linguistics as an adequate method for diachronic studies interested in culturally determined developments of conceptualisations.
A comprehensive study on seismic hazard and earthquake triggering is crucial for effective mitigation of earthquake risks. The destructive nature of earthquakes motivates researchers to work on forecasting despite the apparent randomness of the earthquake occurrences. Understanding their underlying mechanisms and patterns is vital, given their potential for widespread devastation and loss of life. This thesis combines methodologies, including Coulomb stress calculations and aftershock analysis, to shed light on earthquake complexities, ultimately enhancing seismic hazard assessment.
The Coulomb failure stress (CFS) criterion is widely used to predict the spatial distributions of aftershocks following large earthquakes. However, uncertainties associated with CFS calculations arise from non-unique slip inversions and unknown fault networks, particularly due to the choice of the assumed aftershocks (receiver) mechanisms. Recent studies have proposed alternative stress quantities and deep neural network approaches as superior to CFS with predefined receiver mechanisms. To challenge these propositions, I utilized 289 slip inversions from the SRCMOD database to calculate more realistic CFS values for a layered-half space and variable receiver mechanisms. The analysis also investigates the impact of magnitude cutoff, grid size variation, and aftershock duration on the ranking of stress metrics using receiver operating characteristic (ROC) analysis. Results reveal the performance of stress metrics significantly improves after accounting for receiver variability and for larger aftershocks and shorter time periods, without altering the relative ranking of the different stress metrics.
To corroborate Coulomb stress calculations with the findings of earthquake source studies in more detail, I studied the source properties of the 2005 Kashmir earthquake and its aftershocks, aiming to unravel the seismotectonics of the NW Himalayan syntaxis. I simultaneously relocated the mainshock and its largest aftershocks using phase data, followed by a comprehensive analysis of Coulomb stress changes on the aftershock planes. By computing the Coulomb failure stress changes on the aftershock faults, I found that all large aftershocks lie in regions of positive stress change, indicating triggering by either co-seismic or post-seismic slip on the mainshock fault.
Finally, I investigated the relationship between mainshock-induced stress changes and associated seismicity parameters, in particular those of the frequency-magnitude (Gutenberg-Richter) distribution and the temporal aftershock decay (Omori-Utsu law). For that purpose, I used my global data set of 127 mainshock-aftershock sequences with the calculated Coulomb Stress (ΔCFS) and the alternative receiver-independent stress metrics in the vicinity of the mainshocks and analyzed the aftershocks properties depend on the stress values. Surprisingly, the results show a clear positive correlation between the Gutenberg-Richter b-value and induced stress, contrary to expectations from laboratory experiments. This observation highlights the significance of structural heterogeneity and strength variations in seismicity patterns. Furthermore, the study demonstrates that aftershock productivity increases nonlinearly with stress, while the Omori-Utsu parameters c and p systematically decrease with increasing stress changes. These partly unexpected findings have significant implications for future estimations of aftershock hazard.
The findings in this thesis provides valuable insights into earthquake triggering mechanisms by examining the relationship between stress changes and aftershock occurrence. The results contribute to improved understanding of earthquake behavior and can aid in the development of more accurate probabilistic-seismic hazard forecasts and risk reduction strategies.
We study the effect of energy and transport policies on pollution in two developing country cities. We use a quantitative equilibrium model with choice of housing, energy use, residential location, transport mode, and energy technology. Pollution comes from commuting and residential energy use. The model parameters are calibrated to replicate key variables for two developing country cities, Maputo, Mozambique, and Yogyakarta, Indonesia. In the counterfactual simulations, we study how various transport and energy policies affect equilibrium pollution. Policies may be induce rebound effects from increasing residential energy use or switching to high emission modes or locations. In general, these rebound effects tend to be largest for subsidies to public transport or modern residential energy technology.
While the economic harm of cartels is caused by their price-increasing effect, sanctioning by courts rather targets at the underlying process of firms reaching a price-fixing agreement. This paper provides experimental evidence on the question whether such sanctioning meets the economic target, i.e., whether evidence of a collusive meeting of the firms and of the content of their communication reliably predicts subsequent prices. We find that already the mere mutual agreement to meet predicts a strong increase in prices. Conversely, express distancing from communication completely nullifies its otherwise price-increasing effect. Using machine learning, we show that communication only increases prices if it is very explicit about how the cartel plans to behave.
This thesis presents a comprehensive exploration of the application of DNA origami nanofork antennas (DONAs) in the field of spectroscopy, with a particular focus on the structural analysis of Cytochrome C (CytC) at the single-molecule level. The research encapsulates the design, optimization, and application of DONAs in enhancing the sensitivity and specificity of Raman spectroscopy, thereby offering new insights into protein structures and interactions.
The initial phase of the study involved the meticulous optimization of DNA origami structures. This process was pivotal in developing nanoscale tools that could significantly enhance the capabilities of Raman spectroscopy. The optimized DNA origami nanoforks, in both dimer and aggregate forms, demonstrated an enhanced ability to detect and analyze molecular vibrations, contributing to a more nuanced understanding of protein dynamics.
A key aspect of this research was the comparative analysis between the dimer and aggregate forms of DONAs. This comparison revealed that while both configurations effectively identified oxidation and spin states of CytC, the aggregate form offered a broader range of detectable molecular states due to its prolonged signal emission and increased number of molecules. This extended duration of signal emission in the aggregates was attributed to the collective hotspot area, enhancing overall signal stability and sensitivity.
Furthermore, the study delved into the analysis of the Amide III band using the DONA system. Observations included a transient shift in the Amide III band's frequency, suggesting dynamic alterations in the secondary structure of CytC. These shifts, indicative of transitions between different protein structures, were crucial in understanding the protein’s functional mechanisms and interactions.
The research presented in this thesis not only contributes significantly to the field of spectroscopy but also illustrates the potential of interdisciplinary approaches in biosensing. The use of DNA origami-based systems in spectroscopy has opened new avenues for research, offering a detailed and comprehensive understanding of protein structures and interactions. The insights gained from this research are expected to have lasting implications in scientific fields ranging from drug development to the study of complex biochemical pathways. This thesis thus stands as a testament to the power of integrating nanotechnology, biochemistry, and spectroscopic techniques in addressing complex scientific questions.
This study on the Messianic Jewish movement and its relationship to the Torah explores the various aspects of the relationship to the Torah on the basis of 10 interviews with selected Yeshua-believing Jews in leadership positions. The selection of interviewees results in a range of different positions typical of the movement as a whole, which overlap in many respects but are often fundamentally different and sometimes contradictory. Particular attention is paid to the theologically based, divergent and contradictory positions in an attempt to make these understandable.
After a brief introduction to the Messianic Jewish movement, aspects of the Messianic Jewish dual identity are examined and their relevance for the relationship to the Torah is demonstrated. This is followed by an overview of the forums in which Yeshua-believing Jews discuss their relationship to the Torah. The extensive bibliography at the end of the work provides an insight into a lively discussion process within the movement that is still far from complete. A briefly annotated differentiation of terms serves as an overview of the most important meanings of Torah used in the Messianic Jewish movement. Following this preliminary work, the field study is presented. A description of the research field and methodological reflections precede the interviews. In the interviews, the associations with the term Torah are first recorded and the conceptual meaning and use clarified. This already reveals some serious differences. The theological positions and understandings of Torah are presented with the biographical context and main field of influence, and the most important formative influences are named. The points on which they all agree are noted first, as they serve as a common basis. All study the written Torah and consider it, as well as the rest of the Tanakh and the writings of the New Testament in their present form, to be divinely inspired and authoritative. All have found a positive approach to the Torah according to their own definition of the term. For all of them, the written Torah and the Tanakh point to Yeshua. All agree that Yeshua did not abrogate the Torah, but fulfilled it. And all feel a responsibility as a Jew to the Torah in some way. With regard to keeping commandments, all say that no one can earn their way to heaven by doing so. G-d's faithfulness to His promises to Israel is affirmed by all, but whether the new covenant in Yeshua superseded the old covenant of Mt. Sinai, or whether it is simply added to the already existing covenant of Sinai, whether ritual commandments are to continue to be kept after Yeshua's death and resurrection and the destruction of the Temple, whether the commandments aiming at separation from the nations should continue to be kept, whether and under what conditions rabbinic halacha should be followed and what individuals do and teach in their families and communities - all this is discussed interview by interview. It becomes clear how different ways of reading and weighting key scriptures produce different positions. Just as the diversity of positions in relation to the Torah already suggests, the interview partners are divided on the question of a Messianic Jewish Halacha. But here too, the term halacha is interpreted differently by the representatives. At the end of the field study, the attempts to produce Messianic Jewish Halacha and the problems and points of criticism expressed by other interviewees are explained. The work concludes with a theological framework able to contain all the different positions and relationships to the Torah and some starting points for a possible Messianic Jewish hermeneutic theology of the Torah.
Knowledge about causal structures is crucial for decision support in various domains. For example, in discrete manufacturing, identifying the root causes of failures and quality deviations that interrupt the highly automated production process requires causal structural knowledge. However, in practice, root cause analysis is usually built upon individual expert knowledge about associative relationships. But, "correlation does not imply causation", and misinterpreting associations often leads to incorrect conclusions. Recent developments in methods for causal discovery from observational data have opened the opportunity for a data-driven examination. Despite its potential for data-driven decision support, omnipresent challenges impede causal discovery in real-world scenarios. In this thesis, we make a threefold contribution to improving causal discovery in practice.
(1) The growing interest in causal discovery has led to a broad spectrum of methods with specific assumptions on the data and various implementations. Hence, application in practice requires careful consideration of existing methods, which becomes laborious when dealing with various parameters, assumptions, and implementations in different programming languages. Additionally, evaluation is challenging due to the lack of ground truth in practice and limited benchmark data that reflect real-world data characteristics.
To address these issues, we present a platform-independent modular pipeline for causal discovery and a ground truth framework for synthetic data generation that provides comprehensive evaluation opportunities, e.g., to examine the accuracy of causal discovery methods in case of inappropriate assumptions.
(2) Applying constraint-based methods for causal discovery requires selecting a conditional independence (CI) test, which is particularly challenging in mixed discrete-continuous data omnipresent in many real-world scenarios. In this context, inappropriate assumptions on the data or the commonly applied discretization of continuous variables reduce the accuracy of CI decisions, leading to incorrect causal structures.
Therefore, we contribute a non-parametric CI test leveraging k-nearest neighbors methods and prove its statistical validity and power in mixed discrete-continuous data, as well as the asymptotic consistency when used in constraint-based causal discovery. An extensive evaluation of synthetic and real-world data shows that the proposed CI test outperforms state-of-the-art approaches in the accuracy of CI testing and causal discovery, particularly in settings with low sample sizes.
(3) To show the applicability and opportunities of causal discovery in practice, we examine our contributions in real-world discrete manufacturing use cases. For example, we showcase how causal structural knowledge helps to understand unforeseen production downtimes or adds decision support in case of failures and quality deviations in automotive body shop assembly lines.